Text Classification
Transformers
ONNX
Safetensors
multilingual
xlm-roberta
privacy
pii-detection
text-embeddings-inference
Instructions to use Roblox/roblox-pii-classifier-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Roblox/roblox-pii-classifier-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Roblox/roblox-pii-classifier-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Roblox/roblox-pii-classifier-v2") model = AutoModelForSequenceClassification.from_pretrained("Roblox/roblox-pii-classifier-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Overview
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We present `Roblox/roblox-pii-classifier-v2`, an upgraded, context-aware PII (Personally Identifiable Information) detection model. Built on the [XLM-RoBERTa-Large](https://huggingface.co/xlm-roberta-large) architecture, v2 is designed to identify attempts to share or solicit personal information across complex, multi-user conversations.
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Unlike its predecessor and other NER models, **v2
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## What’s New in v2.0?
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The table below illustrates best f1 performance across internal and open-source evaluation benchmarks (full conversational data used when possible):
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| Dataset / Benchmark | PII v2
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| **roblox-pii-safety-for-chat** | **88.78%** | 64.74% | 58.10% | 54.42% | 66.15% | 27.80% | 56.06% | 54.30% | 56.35% | 58.34% | 57.66 |
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| **Roblox Internal Evaluation** | **90.52%** | 63.41% | 20.98% | 28.21% | 15.62% | 8.59% | 12.90% | 21.92% | 14.77% | 20.98% | 31.87 |
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## Overview
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We present `Roblox/roblox-pii-classifier-v2`, an upgraded, context-aware PII (Personally Identifiable Information) detection model. Built on the [XLM-RoBERTa-Large](https://huggingface.co/xlm-roberta-large) architecture, v2 is designed to identify attempts to share or solicit personal information across complex, multi-user conversations.
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Unlike its predecessor and other NER models, **v2 evaluates text within its surrounding conversational context**, allowing it to detect sophisticated, adversarial bypass attempts (e.g., collaborative PII exchange, phonetic spelling, implicit references) while drastically reducing false positives. It is intended for use in casual conversations, ensuring freedom of speech while also maintaining safety.
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## What’s New in v2.0?
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The table below illustrates best f1 performance across internal and open-source evaluation benchmarks (full conversational data used when possible):
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| Dataset / Benchmark | Roblox PII v2 | *Roblox PII v1 | *OpenAI Privacy Filter | *GLiNER2 | Qwen3Guard | LlamaGuard v3 1B | LlamaGuard v3 8B | LlamaGuard v4 12B | NemoGuard 8B | *Piiranha NER | Shieldstral
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| **roblox-pii-safety-for-chat** | **88.78%** | 64.74% | 58.10% | 54.42% | 66.15% | 27.80% | 56.06% | 54.30% | 56.35% | 58.34% | 57.66 |
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| **Roblox Internal Evaluation** | **90.52%** | 63.41% | 20.98% | 28.21% | 15.62% | 8.59% | 12.90% | 21.92% | 14.77% | 20.98% | 31.87 |
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